Argentina vs Saudi Arabia

Argentina vs Saudi Arabia: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 181.5 nights in Saudi Arabia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Saudi Arabia, by 138.9 nights, or 4.26 times that in Argentina.

nights a year · CMIP6

Argentina vs Saudi Arabia: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 181.5 nights in Saudi Arabia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Saudi Arabia, by 138.9 nights, or 4.26 times that in Argentina.
ArgentinaSaudi ArabiaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Saudi Arabia has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Argentina at 65.2 nights and Saudi Arabia at 218.7 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaSaudi Arabia
201442.6181.5
201341.8180.4
201241182.8
201141.5182.7
201040.8179.2
200941.6177.1
200841.4180.3
200739.6178.9
200639.3177.5
200540.1177.9
200440.6177.6
200338.7177.4
200240.2175.6
200139.5178.1
200039.5176.4
199938.4173.2
199838.8173.4
199738.5173.8
199637172
199535.7169.2
199434.7170.4
199334.3164.6
199233.4165.9
199136.8169.2
199036172.7
198937168.9
198836.9166.8
198736.1167.3
198636.4169.4
198536166
198434.7165
198335.2163.5
198235.7164.7
198137.3168.9
198036.3166.8
197937.5165.3
197835.7164.8
197735.1165.6
197635.7165.9
197534.8161.2
197435.4162.9
197334.1163.7
197235.5163.7
197134.7160.4
197034.9162.5
196933.4161.3
196834.6162.7
196733159.5
196634.3159.5
196532.2159.5
196430.3159.9
196332160.2
196235.2163.2
196135.6163.7
196035.6165.7
195934.5164.4
195833.3164.1
195735.7165.5
195635163.6
195534.4162
195433.6161.2
195335.8162.5
195234.4163.4
195135.4161.2
195033.9161.6
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaSaudi Arabia
201544.433.5–53.4184.9169.4–198.7
201643.532.4–53.4183167.7–195.7
201742.834.2–52.6182.6168.1–196.7
201842.632.7–52.6186.8173.1–199.4
201943.633.4–53.1184.1168.8–199.1
202045.735.5–57.2185.3171.8–198.9
202145.634.3–56.4188.6173.6–202.8
202244.633.3–56.4187.6170.7–202
202344.934.9–53.9188.6173–203.8
202445.834.2–57.5187.1172.9–203.9
202546.536–58.1191.5176–206.1
202647.936.7–57191.8177.1–205.5
202746.434.5–56.3191.2176.6–206.8
202846.133.5–58.5191.9175.5–205.1
202948.336–58.8192.2176.6–205.7
203047.835.4–60.3193.3178.4–206.3
203146.935.5–58.7194.7179.1–207.6
20324837.9–59.7192.2175.2–206
203347.436.7–57.6192.6177.5–206.9
203447.837.8–58.9196.7181.6–213
203547.339–59196.5179.9–211.6
203648.939.2–58.4194.1180–209.7
20375141.2–60.9197183.7–209.9
203848.737.9–59.7198.4182.3–213.3
203948.839.1–58.4198184.4–213.6
204050.440.4–62.3199.2182.1–212.7
204150.740.4–61.7197.7181.4–211.7
20425037.3–61.1198.3181.2–217
204350.340.5–61198.2182.3–215.3
20445240–65199.2185.5–213.5
204552.441.3–65.6202187.1–216.8
204651.941.2–62.7204.1185.3–219.4
204751.542–64204.1187.4–220
204854.542.6–65206.1190.7–220
204953.942.8–64.9201.6187.7–216.2
205052.543.6–62.4205.7190.2–219.3
205155.743.3–67.4204.3188.8–222.1
205255.642.2–67203.8189.6–218
205355.342.6–67.6203.8186.3–218.8
205454.743–66.7207.6188.3–222.3
205555.942.3–67.2207.8190.8–221
205657.642.8–68.6203.7186.6–220.1
205756.842.3–68.7207.5188.8–224.2
205854.841.5–68.1205.9189.2–221.4
205957.844.9–70.3207.3191.6–226.9
206056.943.4–67.9206.9192.7–222.8
206156.542.3–70.6206.5190.4–220.9
206257.345.8–68.5208.1189.4–228.3
206358.244.6–70.2210.4193–227.1
206458.845.8–71.5207.5191.9–223.3
206558.746–71.1208.9193.9–226.1
206659.947–68.9210.3194.9–228
206758.944.8–71.5210.9193.1–228.6
206857.844.3–70.3209.8194.6–228.6
206959.445.4–70.5208.9194.5–226.3
207061.348.2–74.3211.2191.2–229
207160.148.7–73.2215.1196.3–233.3
207260.146.7–73.7213.6195.3–229.3
207360.847.6–73.5213.7194.1–231.6
207460.447.6–73.7210.5191.9–229.3
207561.347.9–73.9212.1196.6–231.8
207661.146.8–75.8212.8194.8–230.3
207760.647.1–73.1215.1196.6–233.9
207863.449.4–75.6215.5197.6–230.7
207959.948.7–73213.6197–229
20806348.1–75.5213.6197.1–233.9
208161.448.7–73.5214.7194.4–231.6
208261.747.7–75214198.9–230.9
208362.148.2–75.6214.2198.5–238
208462.950.3–76217.4197.4–233.1
20856246.9–76.4217.4199.6–237.9
208662.749.4–76.4217197.4–237.4
208761.847.9–74.9217.3199.7–238.7
208864.450.8–77.5216.1198.2–243.1
208962.749.3–76.3217.6199.5–239
209064.152.5–77.1217.3198.5–239.2
209164.451.7–78.3218.5202.8–238.4
209265.650.8–78.3218.5198.5–241.6
209362.749.7–76.8216.6197.8–236.3
209463.950.1–78.5218.8202–236.9
209562.948.8–77.9219.4204.2–237.4
209664.749.4–78.9219.9197.7–239.6
209764.552.4–80.1216.7196.8–239.7
209865.652.7–78.7219.7199.6–241.3
209965.249.3–80.2216.8200.8–236.8
210065.250–81.7218.7202.3–241

Each country on its own

Good to know

  • Nights in a year when the temperature stays above 20 °C, averaged over the country.
  • Source: World Bank Climate Change Knowledge Portal, CMIP6 multi-model median.
  • The dashed line is the CMIP6 model median (SSP2-4.5, middle of the road), a scenario rather than a forecast; the band is its models’ 10th–90th percentile.
  • Climate model values are the median of the CMIP6 models, run on past emissions to 2014 and on SSP2-4.5, a middle-of-the-road path, after: they are not measurements.

How the layers work, where their data comes from and what they can’t tell you: Countries through time

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